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'''[[Environmental informatics]]''' ('''EI''') is a developing field of science that applies [[information]] processing, management, and sharing strategies to the interdisciplinary field of environmental science. Applications include the integration of information and knowledge, the application of computational intelligence to environmental data, and the identification of the environmental impacts of information technology. EI helps scientists define information processing requirements, analyze real-world problems, and solve those problems using informatics methodologies and tools.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


As EI has continued to evolve, several other definitions have been offered over the years. Some consider it "an emerging field centering around the development of standards and protocols, both technical and institutional, for sharing and integrating environmental data and information." Others consider it the application of "[r]esearch and system development focusing on the environmental sciences relating to the creation, collection, storage, processing, modelling, interpretation, display and dissemination of data and information." ('''[[Environmental informatics|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
 
''Recently featured'':
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{{flowlist |
''Recently featured'': [[Application programming interface]], [[Immunoinformatics]], [[Life sciences industry]]
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
Recently featured:

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